Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. This week, we’ll talk about what digital twin technology is and how IoT has brought it closer to us.

While digital twin technology has been around for over a decade, it wasn’t until more recently that organizations began implementing it. But what are digital twins and what do they do? Simply put, a digital twin is a digital replica of physical assets, processes, people, places, systems, or devices. Digital twins use sensors attached to physical objects to collect and transmit data about those physical objects.

A digital twin can optimize the physical object’s performance by providing a test environment for maintenance operations, so you can see how these changes perform before they’re implemented on the physical object itself. Interestingly, digital twin technology helps you control not only the physical elements, but also the real-time dynamics of how an object or a system interacts with other elements in the physical environment, as well as with the environment itself.

Let’s say your car has a digital twin; the sensors within the engine will be able to recognize parts that need to be repaired or changed. This data gets transmitted in real-time to its digital twin—so you’ll know when it’s time to bring your car in for maintenance and what exactly needs to be checked. Your car’s digital twin not only notifies you when something’s up, but also allows your mechanic to test alternative components or processes before trying them on the car itself. This will both speed up the entire maintenance process and make it more cost effective.

Now, imagine this technology implemented across multiple machines and processes in a large manufacturing factory. The entire factory can be digitally replicated, and any process change or workflow enhancement can be tried and tested digitally without hindering the everyday working of the factory. Since digital twins are also capable of utilizing machine learning, the longer you use them, the better they become at identifying errors and offering solutions. Hypothetically, machine learning-enabled digital twins could even learn to predict problems before they occur.

That said, here are five interesting reads on what digital twin technology is and how it can impact the way businesses operate in the future.

Though virtualization of assets and prediction of asset behavior can make things easier for businesses, there are quite a few challenges when it comes to deploying digital twins. Data has to be fed to the twin in real time throughout the life cycle of a product and this has to happen seamlessly.

Digital twin technology is a perfect fusion of software, hardware, and IoT-enabled feedback. It tracks every move an object makes and predicts plausible outcomes of the movement as well. Once businesses master this technology, analytics and trails will be much simpler, and in no time digital twins will begin to trickle down into every day processes and products.

All kinds of businesses, from huge industrial giants to small entrepreneurs, have begun to embrace AI and virtualization. Predictive analytics is becoming essential, especially with how widespread IoT has become across industries, so it’s inevitable that digital twin technology will change various aspects of business.

Though digital twin technology has been around for quite some time now, it has always been considered a complex, far-fetched technology, but that’s all changing. With the capabilities this technology offers today, it’s nearly impossible to ignore it in your IoT toolkit.

The process of creating a twin of every physical object through a digital thread and retrieving information through sensors isn’t as easy as it sounds. On top of that, too many devices combined with complex data creates a number of challenges, too. This means organizations need to have a future-ready strategy in place.

Data is the key to success for any business in today’s day and age. With the help of IoT and machine learning, predictive data analytics has become much easier. And now, digital twins are getting a step ahead with the ability to stay connected to physical assets throughout their life cycles. With more data points and better analytics, businesses will look to their past and the future to not only solve design and operational issues, but also improve their customers’ experience.